Data uncertainty is widespread in a variety of applications. This paper proposes a new Bayesian classification algorithm for classifying uncertain data.
Data uncertainty is widespread in a variety of applications. This paper proposes a new Bayesian classification algorithm for classifying uncertain data.
This paper proposes a new Bayesian classification algorithm for classifying uncertain data. In the paper, we apply prob- ability and statistics theory on ...
This paper applies probability and statistics theory on uncertain data model, and provides solutions for model parameter estimation for both uncertain ...
The Naive Bayesian classifier estimates the class-conditional probability by assuming that the attributes are conditionally independent, given the class label C ...
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This paper proposes a novel naïve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by ...
In this paper, we propose a novel Bayesian classification for classifying and predicting uncertain data sets. Uncertain data are extensively presented in modern ...
In this paper, we are using probabilistic models on uncertain data and develop a novel method to calculate conditional probabilities for uncertain numerical ...
Naive Bayes classifiers are a family of linear probabilistic classifiers which assumes that the features are conditionally independent, given the target class.
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Experimental results in real uncertain data streams prove that our density-based naive classifier is efficient, accurate, and robust to data uncertainty. I.